A multi-functional peripheral equipment (to be referred to as “MFP” hereinafter) which has copy, printer, and facsimile functions has long been around. In the MFP, the copy function is implemented by applying image processing to an image scanned by a scanner, and outputting it to a printer. The printer function is implemented by receiving print data generated by a host computer, applying rendering and image processes to the print data, and outputting it to the printer.
In recent years, the image resolution of the scanner and printer of the MFP is 600 dpi, 1200 dpi, or the like, and the number of pages that can be processed per minute is as many as 50 color pages and 100 monochrome pages in case of a faster one. For this reason, large-size image data must be processed at high speed, and the image processing of the MFP is implemented by hardware using a dedicated image processing LSI (ASIC).
The image processing by means of hardware (to be referred to as “hardware image processing” hereinafter) can meet a high-speed requirement, but it has demerits such as hard modifiability and poor flexibility, high cost, and the like. When the logic of the already prepared image processing LSI is to be slightly modified, it requires much time and cost. For this reason, studies have been made to implement various kinds of image processing by software with high flexibility. To speed up upon implementing the image processing by means of software (to be referred to as “software image processing” hereinafter), it is effective to execute distributed processing by dividing large-size image data, and a plurality of proposals have been made.
In recent years, parallel distributed processing computing techniques such as PC clustering, grid computing, and the like have been developed, and development of these techniques has an advantage upon implementing the software image processing. In the grid computing technique, procedures required to use computation resources on a network are simple. As long as a grid is built on the network, computation resources can be used very easily. With this technique, an image processing system which can enjoy merits such as flexibility of the software image processing, and easy management obtained from the distributed processing technique (especially, grid computing) can be proposed.
However, even when a CPU gains higher performance and a distributed computing environment is being put in place, it is not easy to follow the speed of the hardware image processing implemented by the dedicated hardware. In the parallel distributed processing environment, it is indispensable to configure the system so that it can execute higher-speed processing as much as possible.